{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyO4F41u8CvKQJ1zu4U/wjD+",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SJinLee/PPG/blob/main/05.yurim_data0418.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 데이터만 준비된 상태\n",
        "* HRV analysis 는 다음 사이트에서 계산법을 찾을 수 있음\n",
        "\n",
        "https://github.com/trevorwitter/Baroreflex/blob/master/main.py\n",
        "\n",
        "https://github.com/Aura-healthcare/hrv-analysis"
      ],
      "metadata": {
        "id": "vsDQ9Z5hYIYE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4rInmP6i_OmU"
      },
      "outputs": [],
      "source": [
        "from scipy import io\n",
        "mat_file = io.loadmat('yurim0418_8min.mat')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mat_file"
      ],
      "metadata": {
        "id": "0FpVp9dMYd6O",
        "outputId": "a24a4fd9-c0ed-4b67-b00f-2a0bc0d0001b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'__header__': b'MATLAB 5.0 MAT-file, Platform: PCWIN64, Created on: Thu Apr 18 10:21:18 2024',\n",
              " '__version__': '1.0',\n",
              " '__globals__': [],\n",
              " 'data': array([[482, 484, 486, ..., 493, 494, 494]], dtype=uint16)}"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = mat_file['data']"
      ],
      "metadata": {
        "id": "FefRZux8Yuws"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(data[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "U_Ru8mCVAUWM",
        "outputId": "1a259df3-59c6-4fa7-dfb3-3ffed0acdc91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7e1eac7da9e0>]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "umYIHmMiZIh2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_lowpass(ppg):\n",
        "    ppg2 = ppg.copy()\n",
        "    d = 0.92\n",
        "    a = d\n",
        "    b = 1-d\n",
        "    xi = ppg2['Red_Signal'].values[1:].astype(float).copy()\n",
        "    v = ppg2['Red_Signal'].values.astype(float).copy()\n",
        "    y = v[0]\n",
        "    i = 1\n",
        "    for x in xi:\n",
        "        y += b*(x-y)\n",
        "        v[i] = y\n",
        "        i += 1\n",
        "    ppg2['Red_Signal2'] = v\n",
        "    ppg2['Time'] = pd.to_datetime(ppg2['Time'])\n",
        "    return ppg2"
      ],
      "metadata": {
        "id": "a6iIylxhDCw5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "data2 = pd.DataFrame({'Red_Signal':data[0]})\n",
        "data2['Time'] = pd.to_datetime('2024-04-11 00:00:00')+pd.Timedelta(milliseconds=1000/790)*data2.index\n",
        "data3 = get_lowpass(data2)\n",
        "diff=data3['Red_Signal2'].diff()\n",
        "data3['peak'] = np.concatenate([[False],(diff.iloc[1:-1].values>0) & (diff.iloc[2:].values<0),[False]])\n",
        "data3['peak2'] = data3['peak'] & (data3['Red_Signal']>600)"
      ],
      "metadata": {
        "id": "4s1nv3wrDD5m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "h9n-ZyemyNtW",
        "outputId": "65ec8005-6f4f-431f-9fdd-7a254d25ad1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Red_Signal                          Time  Red_Signal2   peak  peak2\n",
              "0              482 2024-04-11 00:00:00.000000000   482.000000  False  False\n",
              "1              484 2024-04-11 00:00:00.001265822   482.160000  False  False\n",
              "2              486 2024-04-11 00:00:00.002531644   482.467200  False  False\n",
              "3              491 2024-04-11 00:00:00.003797466   483.149824  False  False\n",
              "4              492 2024-04-11 00:00:00.005063288   483.857838  False  False\n",
              "...            ...                           ...          ...    ...    ...\n",
              "479995         491 2024-04-11 00:10:07.588230890   477.586224  False  False\n",
              "479996         494 2024-04-11 00:10:07.589496712   478.899326  False  False\n",
              "479997         493 2024-04-11 00:10:07.590762534   480.027380  False  False\n",
              "479998         494 2024-04-11 00:10:07.592028356   481.145190  False  False\n",
              "479999         494 2024-04-11 00:10:07.593294178   482.173575  False  False\n",
              "\n",
              "[480000 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f02c3658-9dee-4b82-b09f-1bf9ddd964b3\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Red_Signal</th>\n",
              "      <th>Time</th>\n",
              "      <th>Red_Signal2</th>\n",
              "      <th>peak</th>\n",
              "      <th>peak2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>482</td>\n",
              "      <td>2024-04-11 00:00:00.000000000</td>\n",
              "      <td>482.000000</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>484</td>\n",
              "      <td>2024-04-11 00:00:00.001265822</td>\n",
              "      <td>482.160000</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>486</td>\n",
              "      <td>2024-04-11 00:00:00.002531644</td>\n",
              "      <td>482.467200</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>491</td>\n",
              "      <td>2024-04-11 00:00:00.003797466</td>\n",
              "      <td>483.149824</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>492</td>\n",
              "      <td>2024-04-11 00:00:00.005063288</td>\n",
              "      <td>483.857838</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>479995</th>\n",
              "      <td>491</td>\n",
              "      <td>2024-04-11 00:10:07.588230890</td>\n",
              "      <td>477.586224</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>479996</th>\n",
              "      <td>494</td>\n",
              "      <td>2024-04-11 00:10:07.589496712</td>\n",
              "      <td>478.899326</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>479997</th>\n",
              "      <td>493</td>\n",
              "      <td>2024-04-11 00:10:07.590762534</td>\n",
              "      <td>480.027380</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>479998</th>\n",
              "      <td>494</td>\n",
              "      <td>2024-04-11 00:10:07.592028356</td>\n",
              "      <td>481.145190</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>479999</th>\n",
              "      <td>494</td>\n",
              "      <td>2024-04-11 00:10:07.593294178</td>\n",
              "      <td>482.173575</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>480000 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f02c3658-9dee-4b82-b09f-1bf9ddd964b3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f02c3658-9dee-4b82-b09f-1bf9ddd964b3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f02c3658-9dee-4b82-b09f-1bf9ddd964b3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a291bdfb-1dfe-4ec4-9087-4963b4ede470\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a291bdfb-1dfe-4ec4-9087-4963b4ede470')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a291bdfb-1dfe-4ec4-9087-4963b4ede470 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data3"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "def draw_ppg(ppg2,start,period):\n",
        "    t = ppg2['Time'][start:start+period]\n",
        "    x = ppg2['Red_Signal'][start:start+period]\n",
        "    y = ppg2['Red_Signal2'][start:start+period]\n",
        "    plt.plot(t,x,label='input')\n",
        "    plt.plot(t,y,label='output')\n",
        "    if 'peak2' in ppg2:\n",
        "        peak = ppg2['peak2'][start:start+period]\n",
        "        plt.plot(t.loc[peak],y.loc[peak],'og')\n",
        "    plt.legend()"
      ],
      "metadata": {
        "id": "NULqbLWbzdl8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "draw_ppg(data3,300,5000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "6UKCiZRvyVWT",
        "outputId": "092867f1-540d-4be3-883a-17dc7031532a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* RRInterval"
      ],
      "metadata": {
        "id": "YQLR5ylxcAEI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rrinterval = data3.loc[data3['peak2']]['Time'].diff() / pd.to_timedelta(1, unit='S')\n",
        "rrinterval = rrinterval.values[1:]\n",
        "rrinterval.shape"
      ],
      "metadata": {
        "id": "F3ZtIjZi12R3",
        "outputId": "8e07ffbc-8f72-432b-b773-7eeeb0149c7e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(915,)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "step = 20\n",
        "for i in range(0,len(rrinterval),step):\n",
        "    print(i,rrinterval[i:i+step].mean())"
      ],
      "metadata": {
        "id": "A23m8VVEdGzF",
        "outputId": "56eeee24-5163-4fbe-844c-2c135dedf3fb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 0.6215818931\n",
            "20 0.6365185927000001\n",
            "40 0.6174679716\n",
            "60 0.7146831012\n",
            "80 0.5943034289999999\n",
            "100 0.5924679870999999\n",
            "120 0.5844300174000001\n",
            "140 0.5748730612999999\n",
            "160 0.6230375884\n",
            "180 0.6447464356999999\n",
            "200 0.5950629221999999\n",
            "220 0.65189833\n",
            "240 0.6184806292\n",
            "260 0.6218350575000001\n",
            "280 0.6649995876999999\n",
            "300 0.6421515006\n",
            "320 0.5939236824\n",
            "340 0.5924046960000001\n",
            "360 0.5876578634999999\n",
            "380 0.5712021775\n",
            "400 0.6415818807\n",
            "420 0.8515817505000001\n",
            "440 0.6857590685\n",
            "460 0.6024679809\n",
            "480 0.7839235646\n",
            "500 0.5972148196\n",
            "520 0.8402526435999998\n",
            "540 0.8102526621999999\n",
            "560 0.8803159099\n",
            "580 0.8060121585\n",
            "600 1.2977207144\n",
            "620 0.6556325049\n",
            "640 0.6187337935999999\n",
            "660 0.7527210523\n",
            "680 0.5711388864\n",
            "700 0.6089869642\n",
            "720 0.618986958\n",
            "740 0.6511388368000001\n",
            "760 0.5602528172\n",
            "780 0.5767085031999999\n",
            "800 0.7027210833\n",
            "820 0.6121515192\n",
            "840 0.5946831756\n",
            "860 0.6230375883999999\n",
            "880 0.6296198628\n",
            "900 0.5843878233333334\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tm = rrinterval.cumsum() - rrinterval[0]"
      ],
      "metadata": {
        "id": "d46zvhgkaT45"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* use interpolate1d to do sampling"
      ],
      "metadata": {
        "id": "DJUGdVkRar1l"
      }
    }
  ]
}